%0 Book Section
%@nexthigherunit 8JMKD3MGPCW/3ESGTTP
%3 10.1007@978-3-030-20521-810.pdf
%4 sid.inpe.br/mtc-m21c/2019/07.01.15.22
%A Pineda, Aruane Mello,
%A Ramos, Fernando Manoel,
%A Betting, Luiz Eduardo,
%A Campanharo, Andriana S. L. O.,
%@secondarytype PRE LI
%B Advances in Computational Intelligence
%D 2019
%E Rojas, Ignacio,
%E Joya, Gonzalo,
%E Catala, Andreu,
%@secondarykey INPE--/
%I Springer Verlag
%K complex networks, Alzheimer disease.
%P 115-126
%T Use of complex networks for the automatic detection and the diagnosis of Alzheimer’s disease
%X Alzheimers disease (AD) is classified as a chronic neurological disorder of the brain and affects approximately 25 million elderly individuals worldwide. This disorder leads to a reduction in peoples productivity and imposes restrictions on their daily lives. Studies of AD often rely on electroencephalogram (EEG) signals to provide information on the behavior of the brain. Recently, a map from a time series to a network has been proposed and that is based on the concept of transition probabilities; the series results in a so-called quantile graph (QG). Here, this map, which is also called the QG method, is applied for the automatic detection of healthy patients and patients with AD from recorded EEG signals. Our main goal is to illustrate how the differences in dynamics in the EEG signals are reflected in the topology of the corresponding QGs. Based on various network metrics, namely, the clustering coefficient, the mean jump length and the betweenness centrality, our results show that the QG method can be used as an effective tool for automated diagnosis of Alzheimers disease.
%@area COMP
%@electronicmailaddress aruane.pineda@unesp.br
%@electronicmailaddress fernando.ramos@inpe.br
%@electronicmailaddress luiz.betting@unesp.br
%@electronicmailaddress andriana.campanharo@unesp.br
%@documentstage not transferred
%@group
%@group LABAC-COCTE-INPE-MCTIC-GOV-BR
%@dissemination BNDEPOSITOLEGAL
%@isbn 978-303020520-1
%@usergroup simone
%@affiliation Universidade Estadual Paulista (UNESP)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Universidade Estadual Paulista (UNESP)
%@affiliation Universidade Estadual Paulista (UNESP)
%@versiontype publisher
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}
%@doi 10.1007/978-3-030-20521-8_10
%2 sid.inpe.br/mtc-m21c/2019/07.01.15.22.37